With development of high speed railways, safety issues of motor train units have also received extensive attention. For the study on safety of high speed rail trains, the “frequency-consequence” matrix method is more mature and is also a method used most widely. The frequency and the consequence in the matrix method are given according to experience of experts, which are more subjective.
A support vector machine (SVM) has a simple structure, a fast learning speed, good promotion performance, a unique minimal point during optimization solution and the like. The SVM is proposed in order to solve a two-classification problem; for a multi-classification problem, the SVM algorithm has one disadvantage: when a voting result is a tie, a safety level of a sample cannot be judged correctly. Weighted kNN (k neighbors) is re-judging a sample that cannot be classified accurately by the SVM, that is, for k categories, a category to which a sample point is close is judged, and the sample point is classified into the category.
Compared with the high speed rail safety evaluation method relatively common at present, that is, the matrix method, the weighted kNN-SVM-based safety evaluation method eliminates subjective factors in the matrix method from the position of a component in the system and reliability of the component, and thus has significant practical values and promotion significance for evaluation of high speed rail safety.